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Risk Evaluation of Overseas Mining Investment Based on a Support Vector Machine

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  • Hujun He

    (School of Earth Science and Resources, Chang’an University, Xi’an 710054, China
    Key Laboratory of Western Mineral Resources and Geological Engineering, Ministry of Education, Xi’an 710054, China)

  • Yichen Zhao

    (School of Earth Science and Resources, Chang’an University, Xi’an 710054, China)

  • Hongxu Tian

    (School of Earth Science and Resources, Chang’an University, Xi’an 710054, China)

  • Wei Li

    (School of Earth Science and Resources, Chang’an University, Xi’an 710054, China)

Abstract

Analyzing the general method of establishing a support vector machine evaluation model, this paper discusses the application of this model in the risk assessment of overseas mining investment. Based on the analysis of the risk assessment index system of overseas mining investment, the related parameters of the optimal model were ascertained by training the sample data of 20 countries collected in 2015 and 2016, and the investment risk of 8 test samples was evaluated. All 8 samples were correctly identified, with an error rate of 0. South Africa’s mining investment risk in 2016 was assessed using the risk evaluation model for overseas mining investment based on a support vector machine, and it was rated as grade IV (general investment risk). The results show that the model can provide a new solution for the judgment and deconstruction of the risk of overseas mining investment.

Suggested Citation

  • Hujun He & Yichen Zhao & Hongxu Tian & Wei Li, 2022. "Risk Evaluation of Overseas Mining Investment Based on a Support Vector Machine," Sustainability, MDPI, vol. 15(1), pages 1-14, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:240-:d:1013136
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    References listed on IDEAS

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